{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c98393c4-06a6-42f4-bea7-5ffa3d1b4232",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"transformers\")\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"tqdm\")\n",
    "\n",
    "valid_data = pd.read_csv('./机器翻译质量评估挑战赛公开数据/valid.txt', sep='\\t', header=None)\n",
    "valid_data = valid_data[~valid_data[2].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fe5cd971-df00-4db3-b0f9-374b36ff463b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>在变电所和供电系统的设计和运行中，基于如下用途必须进行短路电流的计算：</td>\n",
       "      <td>In the design and operation of substations and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Prostate Testing for Cancer and Treatment ...</td>\n",
       "      <td>前列腺癌检测与治疗（ProtecT）研究比较了PSA检测出前列腺癌的男性患者的前列腺切除术与...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Particles with nonzero electric charge interac...</td>\n",
       "      <td>电电荷为非零的粒子通过交换光子（电磁力的载体）相互作用。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中国的一个租船人,租了一条10万吨的美国船东的油轮,从上海装货去美国。</td>\n",
       "      <td>A Chinese charterer chartered a 100000 ton tan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>为了节省成本，运营商在5G建网初期都会选择NSA。</td>\n",
       "      <td>In order to save costs, operators will choose ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>在周五黄昏巨浪冲击海岸时，有数百人聚集在海滩庆祝节日，很多人被巨浪冲走，巨浪所到之处一切尽毁。</td>\n",
       "      <td>Hundreds of people gathered on the beach to ce...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>我不确定。但在我们解决之前不要做任何事。</td>\n",
       "      <td>Well, I'm not sure. But don't do anything unti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>在本法规定的范围以外需要悬挂国微或者使用国徽图案的，由全国人民代表大会常务委员会办公厅或者国...</td>\n",
       "      <td>If it is necessary to hang the national emblem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>贸易的吹鼓手们对此负有一定的责任。</td>\n",
       "      <td>The trade drummers are responsible for this.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>苹果 。</td>\n",
       "      <td>Apples .</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     0  \\\n",
       "0                  在变电所和供电系统的设计和运行中，基于如下用途必须进行短路电流的计算：   \n",
       "1    The Prostate Testing for Cancer and Treatment ...   \n",
       "2    Particles with nonzero electric charge interac...   \n",
       "3                  中国的一个租船人,租了一条10万吨的美国船东的油轮,从上海装货去美国。   \n",
       "4                            为了节省成本，运营商在5G建网初期都会选择NSA。   \n",
       "..                                                 ...   \n",
       "495    在周五黄昏巨浪冲击海岸时，有数百人聚集在海滩庆祝节日，很多人被巨浪冲走，巨浪所到之处一切尽毁。   \n",
       "496                               我不确定。但在我们解决之前不要做任何事。   \n",
       "497  在本法规定的范围以外需要悬挂国微或者使用国徽图案的，由全国人民代表大会常务委员会办公厅或者国...   \n",
       "498                                  贸易的吹鼓手们对此负有一定的责任。   \n",
       "499                                               苹果 。   \n",
       "\n",
       "                                                     1  \n",
       "0    In the design and operation of substations and...  \n",
       "1    前列腺癌检测与治疗（ProtecT）研究比较了PSA检测出前列腺癌的男性患者的前列腺切除术与...  \n",
       "2                         电电荷为非零的粒子通过交换光子（电磁力的载体）相互作用。  \n",
       "3    A Chinese charterer chartered a 100000 ton tan...  \n",
       "4    In order to save costs, operators will choose ...  \n",
       "..                                                 ...  \n",
       "495  Hundreds of people gathered on the beach to ce...  \n",
       "496  Well, I'm not sure. But don't do anything unti...  \n",
       "497  If it is necessary to hang the national emblem...  \n",
       "498       The trade drummers are responsible for this.  \n",
       "499                                           Apples .  \n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv('./机器翻译质量评估挑战赛公开数据/test.txt', sep='\\t', header=None)\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f5a2d3ee-b510-46cc-871f-7e9b494446fc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langdetect import detect_langs\n",
    "a = detect_langs(\"屏幕需要稍加区别对待。\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d8ce3cb-d2fa-4438-affd-c1ba9dd290f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.75\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "reference = [['this', 'is', 'two', 'test']]\n",
    "candidate = ['this', 'is', 'a', 'test']\n",
    "score = sentence_bleu(reference, candidate, weights=[1])\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc49e36c-8845-47ad-b277-ce513a56f35a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88148904"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "sentences = [\"This is an example sentence\", \"This is two example sentence\"]\n",
    "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n",
    "embeddings = normalize(model.encode(sentences))\n",
    "\n",
    "embeddings[0].dot(embeddings[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37fa19e5-a7a8-4162-bb48-a2a6a7822e3a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "# pipe = pipeline(\"text2text-generation\", model=\"Helsinki-NLP/opus-mt-zh-en\", device='cuda:0')\n",
    "pipe = pipeline(\"text2text-generation\", model=\"/home/lyz/huggingface-models/Helsinki-NLP/opus-mt-zh-en\", device='cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d25776e-391c-4a7e-8d0f-4241f67c8485",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9659/4261872139.py:2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for row in tqdm_notebook(valid_data.iterrows()):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3bb36a702064d2b903d88daace99000",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = []\n",
    "for row in tqdm_notebook(valid_data.iterrows()):\n",
    "    str1 = row[1].iloc[0]\n",
    "    str2 = row[1].iloc[1]\n",
    "    score = row[1].iloc[2]\n",
    "    \n",
    "    if detect_langs(str1)[0].lang == 'zh-cn':\n",
    "        pass\n",
    "    else:\n",
    "        str1, str2 = str2, str1\n",
    "    \n",
    "    str1_translate = pipe(str1)[0]['generated_text']\n",
    "    bleu_score = sentence_bleu([str2.split()], str1_translate.split(), weights=[1])\n",
    "    \n",
    "    sentences = [str2, str1_translate]\n",
    "    embeddings = normalize(model.encode(sentences))\n",
    "    sentence_score = embeddings[0].dot(embeddings[1])\n",
    "    \n",
    "    result.append([bleu_score, sentence_score, score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8102a695-68a2-4cfb-87c4-b17aa6224ccd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='0', ylabel='2'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=pd.DataFrame(result)[0], y=pd.DataFrame(result)[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d3fa9418-80ea-413f-b90e-870ce4442e0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='1', ylabel='2'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=pd.DataFrame(result)[1], y=pd.DataFrame(result)[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "44e94913-fa2c-422e-a0ba-6e3a7ed8f8fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "m = LinearRegression()\n",
    "m.fit(pd.DataFrame(result).iloc[:, :2], pd.DataFrame(result).iloc[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "104d439b-985a-461f-8699-c09758c63a79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='2'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=m.predict(pd.DataFrame(result).iloc[:, :2]), y=pd.DataFrame(result).iloc[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "892e9103-ae62-44b1-97ec-0fb764cd504b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9659/1336079783.py:2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for row in tqdm_notebook(test_data.iterrows()):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c26801d06d34c298e1582ac7c5569a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = []\n",
    "for row in tqdm_notebook(test_data.iterrows()):\n",
    "    str1 = row[1].iloc[0]\n",
    "    str2 = row[1].iloc[1]\n",
    "    \n",
    "    if detect_langs(str1)[0].lang == 'zh-cn':\n",
    "        pass\n",
    "    else:\n",
    "        str1, str2 = str2, str1\n",
    "    \n",
    "    str1_translate = pipe(str1)[0]['generated_text']\n",
    "    bleu_score = sentence_bleu([str2.split()], str1_translate.split(), weights=[1])\n",
    "    \n",
    "    sentences = [str2, str1_translate]\n",
    "    embeddings = normalize(model.encode(sentences))\n",
    "    sentence_score = embeddings[0].dot(embeddings[1])\n",
    "    \n",
    "    result.append([bleu_score, sentence_score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07ffe98a-b965-4107-bf69-9c3b3be2d585",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.DataFrame({\n",
    "    'uuid': range(500),\n",
    "    'label': m.predict(pd.DataFrame(result).iloc[:, :2]) // 5 * 5\n",
    "}).astype(int).to_csv('sub.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e893537f-120d-4c40-9608-fef7f94c43f0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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